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Masked V-Net: an approach to brain tumor segmentation

Author
Catà, M.; Casamitjana, A.; Sanchez, I.; Combalia, M.; Vilaplana, V.
Type of activity
Presentation of work at congresses
Name of edition
20th International Conference on Medical Image COmputing and Computer Assisted Intervention
Date of publication
2017
Presentation's date
2017-09-14
Book of congress proceedings
2017 International MICCAI BraTS Challenge. Pre-conference proceedings
First page
42
Last page
49
Project funding
Heterogeneous information and graph signal processing for the Big Data era. Application to high-throughput, remote sensing, multimedia and human computer interfaces
Repository
http://hdl.handle.net/2117/118671 Open in new window
https://www.cbica.upenn.edu/sbia/Spyridon.Bakas/MICCAI_BraTS/MICCAI_BraTS_2017_proceedings_shortPapers.pdf Open in new window
Abstract
This paper introduces Masked V-Net architecture, a variant of the recently introduced V-Net[13] that reformulates the residual connections and uses a ROI mask to constrain the network to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions by performing data sampling on the output instead of in the input. We use Masked V-Net in the context of brain tumor segmentation and report results on the BraTS2017 Training and Validation s...
Citation
Catà, M., Casamitjana, A., Sanchez, I., Combalia, M., Vilaplana, V. Masked V-Net: an approach to brain tumor segmentation. A: International Conference on Medical Image Computing and Computer Assisted Intervention. "2017 International MICCAI BraTS Challenge. Pre-conference proceedings". 2017, p. 42-49.
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants